BUFR and GRIB data import and visualization¶
Notebook prepared by Ben Maathuis, Bas Retsios and Willem Nieuwenhuis. ITC-University of Twente, Enschede. The Netherlands
Within this notebook we are going to process data which has been made available through EUMETCast (https://en.wikipedia.org/wiki/Eumetcast). Some of the data provided is in a format commonly used by the meteorological survices, like BUFR (a binary data format standardized by the World Meteorological Organization (WMO) - https://en.wikipedia.org/wiki/BUFR) and GRIB (GRIdded Binary is a data format developed by the World Meteorological Organization (WMO) for storing and exchanging gridded meteorological data - https://en.wikipedia.org/wiki/GRIB).
First we are going to process some atmospheric motion vectorss (AMV), for details on this product see the product navigator at EUMETSAT (https://navigator.eumetsat.int/product/EO:EUM:CM:MSG:MSGAMVE0100?query=amv&results=20&s=advanced)
We are going to use a python library called pybufrkit - see: https://pybufrkit.readthedocs.io/en/latest/
To plot AMV's see also: https://dwikita-ichsana.medium.com/meteorology-101-how-to-plot-wind-map-e43c196edce8
The sample data is available in the zip file "BUFR_GRIB_data" and is expected in the root of the notebook folder
In [1]:
#if pybufrkit is not installed, uncomment the line below
#!pip install pybufrkit
In [2]:
# Note from the module import section in the colde field below the line: from mpl_toolkits.basemap import Basemap
# If you get a message: 'No module named 'mpl_toolkits.basemap', uncomment the line below
#!pip install basemap
In [3]:
#import the modules - libraries
import os
import ilwis
import shutil
import glob
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from matplotlib import cm
import matplotlib.colors as mcolors
from sklearn import preprocessing
from pybufrkit.decoder import Decoder
from pybufrkit.mdquery import MetadataExprParser, MetadataQuerent
from pybufrkit.dataquery import NodePathParser, DataQuerent
import subprocess
from glob import glob
#from osgeo import osr, ogr
import geopandas as gpd
from shapely.geometry import Point
from mpl_toolkits.basemap import Basemap
import warnings
warnings.filterwarnings('ignore')
In [4]:
work_dir = os.getcwd() + '/BUFR_GRIB_data'
#set the working directory for ILWISPy
ilwis.setWorkingCatalog(work_dir)
print(work_dir)
d:\jupyter\notebook_scripts\ilwispy_tutorial\tutorial_release/BUFR_GRIB_data
Load bufr file and review the meta data - content descriptions¶
In [5]:
# Decode a AMV BUFR file
# BUFR input file
file_in = work_dir+r'/W_XX-EUMETSAT-Darmstadt,SING+LEV+SAT,MET10+AMV_C_EUMG_20250714133000_3.bin'
print(file_in)
d:\jupyter\notebook_scripts\ilwispy_tutorial\tutorial_release/BUFR_GRIB_data/W_XX-EUMETSAT-Darmstadt,SING+LEV+SAT,MET10+AMV_C_EUMG_20250714133000_3.bin
In [6]:
#split all messages contained in BUFR file into seperate messages
#check the data folder and note all 'seperate' message files created with extenstion *.bin.0 to *.bin.n
subprocess.run(['pybufrkit', 'split', file_in])
Out[6]:
CompletedProcess(args=['pybufrkit', 'split', 'd:\\jupyter\\notebook_scripts\\ilwispy_tutorial\\tutorial_release/BUFR_GRIB_data/W_XX-EUMETSAT-Darmstadt,SING+LEV+SAT,MET10+AMV_C_EUMG_20250714133000_3.bin'], returncode=0)
In [7]:
#read all messages and append
messages = []
decoder = Decoder()
path = file_in
filelist = glob(path + '.*')
for file in filelist:
with open((file), 'rb') as ins:
bufr_message = decoder.process(ins.read())
messages.append(bufr_message)
In [8]:
# Query the metadata
n_subsets = MetadataQuerent(MetadataExprParser()).query(bufr_message, '%n_subsets')
print(n_subsets)
1062
In [9]:
# Decode only the metadata sections of a BUFR file
result = subprocess.run(['pybufrkit', 'info', file_in], capture_output=True)
result = result.stdout.decode('utf-8')
print(result)
TableGroupKey(tables_root_dir='C:\\python313\\Lib\\site-packages\\pybufrkit\\tables', tables_local_dir='C:\\python313\\Lib\\site-packages\\pybufrkit\\tables', wmo_tables_sn=('0', '0_0', '31'), local_tables_sn=None)
<<<<<< section 0 >>>>>>
start_signature = b'BUFR'
length = 64765
edition = 4
<<<<<< section 1 >>>>>>
section_length = 22
master_table_number = 0
originating_centre = 254
originating_subcentre = 0
update_sequence_number = 0
is_section2_presents = False
flag_bits = '0000000'
data_category = 5
data_i18n_subcategory = 255
data_local_subcategory = 10
master_table_version = 31
local_table_version = 0
year = 2025
month = 7
day = 14
hour = 13
minute = 30
second = 0
local_bytes = b''
<<<<<< section 3 >>>>>>
section_length = 9
reserved_bits = '00000000'
n_subsets = 1516
is_observation = True
is_compressed = True
flag_bits = '000000'
unexpanded_descriptors = [310077]
<<<<<< section 4 >>>>>>
section_length = 64722
reserved_bits = '00000000'
Retrieve parameters from BUFR file¶
In [10]:
# Review the content listing of the unexpanded_descriptors - should be 6 digits!
BUFR_parameter = '310077'
In [11]:
result1 = subprocess.run(['pybufrkit', 'lookup', '-l', BUFR_parameter], capture_output=True)
result1 = result1.stdout.decode('utf-8')
print(result1)
310077
001033 IDENTIFICATION OF ORIGINATING/GENERATING CENTRE
001034 IDENTIFICATION OF ORIGINATING/GENERATING SUB-CENTRE
025061 SOFTWARE IDENTIFICATION AND VERSION NUMBER
025062 DATABASE IDENTIFICATION
001007 SATELLITE IDENTIFIER
002153 SATELLITE CHANNEL CENTRE FREQUENCY
001012 DIRECTION OF MOTION OF MOVING OBSERVING PLATFORM
201138
002026 CROSS-TRACK RESOLUTION
002027 ALONG-TRACK RESOLUTION
201000
002028 SEGMENT SIZE AT NADIR IN X-DIRECTION
002029 SEGMENT SIZE AT NADIR IN Y-DIRECTION
002161 WIND PROCESSING METHOD
002164 TRACER CORRELATION METHOD
002023 SATELLITE-DERIVED WIND COMPUTATION METHOD
008012 LAND/SEA QUALIFIER
008013 DAY/NIGHT QUALIFIER
001124 GRID POINT IDENTIFIER
005001 LATITUDE (HIGH ACCURACY)
006001 LONGITUDE (HIGH ACCURACY)
004001 YEAR
004002 MONTH
004003 DAY
004004 HOUR
004005 MINUTE
004006 SECOND
004086 LONG TIME PERIOD OR DISPLACEMENT
002162 EXTENDED HEIGHT ASSIGNMENT METHOD
007004 PRESSURE
011001 WIND DIRECTION
011002 WIND SPEED
011003 U-COMPONENT
011004 V-COMPONENT
012001 TEMPERATURE/AIR TEMPERATURE
020014 HEIGHT OF TOP OF CLOUD
007024 SATELLITE ZENITH ANGLE
001023 OBSERVATION SEQUENCE NUMBER
104000
....031001 DELAYED DESCRIPTOR REPLICATION FACTOR
002162 EXTENDED HEIGHT ASSIGNMENT METHOD
007004 PRESSURE
012001 TEMPERATURE/AIR TEMPERATURE
020014 HEIGHT OF TOP OF CLOUD
113000
....031001 DELAYED DESCRIPTOR REPLICATION FACTOR
004086 LONG TIME PERIOD OR DISPLACEMENT
002020 SATELLITE CLASSIFICATION
001007 SATELLITE IDENTIFIER
002019 SATELLITE INSTRUMENTS
005042 CHANNEL NUMBER
002153 SATELLITE CHANNEL CENTRE FREQUENCY
005040 ORBIT NUMBER
007024 SATELLITE ZENITH ANGLE
005021 BEARING OR AZIMUTH
002162 EXTENDED HEIGHT ASSIGNMENT METHOD
007004 PRESSURE
012001 TEMPERATURE/AIR TEMPERATURE
020014 HEIGHT OF TOP OF CLOUD
119000
....031001 DELAYED DESCRIPTOR REPLICATION FACTOR
004086 LONG TIME PERIOD OR DISPLACEMENT
004086 LONG TIME PERIOD OR DISPLACEMENT
005001 LATITUDE (HIGH ACCURACY)
006001 LONGITUDE (HIGH ACCURACY)
011003 U-COMPONENT
011004 V-COMPONENT
011113 TRACKING CORRELATION OF VECTOR
025148 COEFFICIENT OF VARIATION
103000
....031001 DELAYED DESCRIPTOR REPLICATION FACTOR
008023 FIRST-ORDER STATISTICS
011003 U-COMPONENT
011004 V-COMPONENT
008023 FIRST-ORDER STATISTICS
103000
....031001 DELAYED DESCRIPTOR REPLICATION FACTOR
020111 X-AXIS ERROR ELLIPSE MAJOR COMPONENT
020112 Y-AXIS ERROR ELLIPSE MINOR COMPONENT
020114 ANGLE OF X-AXIS IN ERROR ELLIPSE
001033 IDENTIFICATION OF ORIGINATING/GENERATING CENTRE
008021 TIME SIGNIFICANCE
007004 PRESSURE
011095 U-COMPONENT OF THE MODEL WIND VECTOR
011096 V-COMPONENT OF THE MODEL WIND VECTOR
008021 TIME SIGNIFICANCE
007004 PRESSURE
011095 U-COMPONENT OF THE MODEL WIND VECTOR
011096 V-COMPONENT OF THE MODEL WIND VECTOR
008021 TIME SIGNIFICANCE
008086 VERTICAL SIGNIFICANCE FOR NWP
007004 PRESSURE
011095 U-COMPONENT OF THE MODEL WIND VECTOR
011096 V-COMPONENT OF THE MODEL WIND VECTOR
008086 VERTICAL SIGNIFICANCE FOR NWP
102004
001044 STANDARD GENERATING APPLICATION
033007 PER CENT CONFIDENCE
008092 MEASUREMENT UNCERTAINTY EXPRESSION
007004 PRESSURE
011003 U-COMPONENT
011004 V-COMPONENT
008092 MEASUREMENT UNCERTAINTY EXPRESSION
033066 AMV QUALITY FLAG
020081 CLOUD AMOUNT IN SEGMENT
020012 CLOUD TYPE
020056 CLOUD PHASE
117000
....031001 DELAYED DESCRIPTOR REPLICATION FACTOR
008023 FIRST-ORDER STATISTICS
020016 PRESSURE AT TOP OF CLOUD
008092 MEASUREMENT UNCERTAINTY EXPRESSION
008003 VERTICAL SIGNIFICANCE (SATELLITE OBSERVATIONS)
012001 TEMPERATURE/AIR TEMPERATURE
008003 VERTICAL SIGNIFICANCE (SATELLITE OBSERVATIONS)
020016 PRESSURE AT TOP OF CLOUD
008092 MEASUREMENT UNCERTAINTY EXPRESSION
025149 OPTIMAL ESTIMATION COST
020016 PRESSURE AT TOP OF CLOUD
020014 HEIGHT OF TOP OF CLOUD
013093 CLOUD OPTICAL THICKNESS
013109 ICE/LIQUID WATER PATH
040038 CLOUD PARTICLE SIZE
008011 METEOROLOGICAL FEATURE
014050 EMISSIVITY
008011 METEOROLOGICAL FEATURE
008023 FIRST-ORDER STATISTICS
In [12]:
# select a relevant parameter
BUFR_parameter = '007004' #note 6 digits
result2 = subprocess.run(['pybufrkit', 'lookup', '-l', BUFR_parameter], capture_output=True)
result2 = result2.stdout.decode('utf-8')
print(result2)
007004 PRESSURE, Pa, -1, 0, 14
In [13]:
#extract a parameter for a given message, note the OrderedDict extracted
LAT_result = DataQuerent(NodePathParser()).query(bufr_message, '005001') # see LATITUDE (HIGH ACCURACY)
print(LAT_result.results)
OrderedDict({0: [23.88546, [[None], [None], [None]]], 1: [24.13825, [[None], [None], [None]]], 2: [24.1485, [[None], [None], [None]]], 3: [24.27572, [[None], [None], [None]]], 4: [24.24514, [[None], [None], [None]]], 5: [23.91972, [[None], [None], [None]]], 6: [23.946, [[None], [None], [None]]], 7: [23.84638, [[None], [None], [None]]], 8: [23.89042, [[None], [None], [None]]], 9: [23.71201, [[None], [None], [None]]], 10: [23.61503, [[None], [None], [None]]], 11: [23.58447, [[None], [None], [None]]], 12: [23.55528, [[None], [None], [None]]], 13: [23.44287, [[None], [None], [None]]], 14: [23.42304, [[None], [None], [None]]], 15: [25.04014, [[None], [None], [None]]], 16: [24.91874, [[None], [None], [None]]], 17: [25.04765, [[None], [None], [None]]], 18: [24.90684, [[None], [None], [None]]], 19: [24.8422, [[None], [None], [None]]], 20: [24.76182, [[None], [None], [None]]], 21: [24.67565, [[None], [None], [None]]], 22: [24.65291, [[None], [None], [None]]], 23: [24.48346, [[None], [None], [None]]], 24: [24.48123, [[None], [None], [None]]], 25: [24.3513, [[None], [None], [None]]], 26: [24.33485, [[None], [None], [None]]], 27: [24.15196, [[None], [None], [None]]], 28: [24.18112, [[None], [None], [None]]], 29: [23.95482, [[None], [None], [None]]], 30: [24.05741, [[None], [None], [None]]], 31: [23.96709, [[None], [None], [None]]], 32: [23.9349, [[None], [None], [None]]], 33: [23.94473, [[None], [None], [None]]], 34: [23.90483, [[None], [None], [None]]], 35: [23.81682, [[None], [None], [None]]], 36: [23.77813, [[None], [None], [None]]], 37: [23.53743, [[None], [None], [None]]], 38: [23.52625, [[None], [None], [None]]], 39: [23.49772, [[None], [None], [None]]], 40: [23.4699, [[None], [None], [None]]], 41: [23.47914, [[None], [None], [None]]], 42: [23.45317, [[None], [None], [None]]], 43: [26.52033, [[None], [None], [None]]], 44: [26.47708, [[None], [None], [None]]], 45: [26.06959, [[None], [None], [None]]], 46: [25.70784, [[None], [None], [None]]], 47: [25.86563, [[None], [None], [None]]], 48: [25.55626, [[None], [None], [None]]], 49: [25.48875, [[None], [None], [None]]], 50: [25.24214, [[None], [None], [None]]], 51: [25.09217, [[None], [None], [None]]], 52: [25.13091, [[None], [None], [None]]], 53: [24.92424, [[None], [None], [None]]], 54: [24.95574, [[None], [None], [None]]], 55: [24.96049, [[None], [None], [None]]], 56: [24.83573, [[None], [None], [None]]], 57: [24.73502, [[None], [None], [None]]], 58: [24.69981, [[None], [None], [None]]], 59: [24.66988, [[None], [None], [None]]], 60: [24.64407, [[None], [None], [None]]], 61: [24.63757, [[None], [None], [None]]], 62: [24.64224, [[None], [None], [None]]], 63: [24.50298, [[None], [None], [None]]], 64: [24.4413, [[None], [None], [None]]], 65: [24.4286, [[None], [None], [None]]], 66: [24.37277, [[None], [None], [None]]], 67: [24.27918, [[None], [None], [None]]], 68: [24.29192, [[None], [None], [None]]], 69: [24.26057, [[None], [None], [None]]], 70: [24.14908, [[None], [None], [None]]], 71: [24.26765, [[None], [None], [None]]], 72: [24.25114, [[None], [None], [None]]], 73: [24.19405, [[None], [None], [None]]], 74: [24.18801, [[None], [None], [None]]], 75: [23.79131, [[None], [None], [None]]], 76: [24.25591, [[None], [None], [None]]], 77: [24.16476, [[None], [None], [None]]], 78: [24.20943, [[None], [None], [None]]], 79: [27.17957, [[None], [None], [None]]], 80: [26.29766, [[None], [None], [None]]], 81: [26.12948, [[None], [None], [None]]], 82: [25.97338, [[None], [None], [None]]], 83: [25.67264, [[None], [None], [None]]], 84: [25.72037, [[None], [None], [None]]], 85: [25.77709, [[None], [None], [None]]], 86: [25.69529, [[None], [None], [None]]], 87: [25.61722, [[None], [None], [None]]], 88: [25.57785, [[None], [None], [None]]], 89: [25.44323, [[None], [None], [None]]], 90: [25.49211, [[None], [None], [None]]], 91: [25.46377, [[None], [None], [None]]], 92: [25.13669, [[None], [None], [None]]], 93: [25.14887, [[None], [None], [None]]], 94: [25.03423, [[None], [None], [None]]], 95: [25.06267, [[None], [None], [None]]], 96: [25.02271, [[None], [None], [None]]], 97: [25.00869, [[None], [None], [None]]], 98: [24.97182, [[None], [None], [None]]], 99: [24.88576, [[None], [None], [None]]], 100: [24.88535, [[None], [None], [None]]], 101: [27.7165, [[None], [None], [None]]], 102: [27.49357, [[None], [None], [None]]], 103: [27.20799, [[None], [None], [None]]], 104: [27.26525, [[None], [None], [None]]], 105: [27.18259, [[None], [None], [None]]], 106: [27.02128, [[None], [None], [None]]], 107: [26.93676, [[None], [None], [None]]], 108: [26.91403, [[None], [None], [None]]], 109: [26.83589, [[None], [None], [None]]], 110: [26.73431, [[None], [None], [None]]], 111: [26.68701, [[None], [None], [None]]], 112: [26.47433, [[None], [None], [None]]], 113: [26.63596, [[None], [None], [None]]], 114: [26.58517, [[None], [None], [None]]], 115: [26.3882, [[None], [None], [None]]], 116: [26.34006, [[None], [None], [None]]], 117: [26.31421, [[None], [None], [None]]], 118: [26.14542, [[None], [None], [None]]], 119: [25.92634, [[None], [None], [None]]], 120: [25.8818, [[None], [None], [None]]], 121: [25.87666, [[None], [None], [None]]], 122: [25.74154, [[None], [None], [None]]], 123: [25.71231, [[None], [None], [None]]], 124: [25.73976, [[None], [None], [None]]], 125: [25.70528, [[None], [None], [None]]], 126: [25.64259, [[None], [None], [None]]], 127: [25.99144, [[None], [None], [None]]], 128: [28.24348, [[None], [None], [None]]], 129: [28.17146, [[None], [None], [None]]], 130: [28.4863, [[None], [None], [None]]], 131: [28.22952, [[None], [None], [None]]], 132: [27.90356, [[None], [None], [None]]], 133: [27.78588, [[None], [None], [None]]], 134: [27.90596, [[None], [None], [None]]], 135: [27.46361, [[None], [None], [None]]], 136: [27.49238, [[None], [None], [None]]], 137: [27.50379, [[None], [None], [None]]], 138: [27.43305, [[None], [None], [None]]], 139: [27.3743, [[None], [None], [None]]], 140: [27.18386, [[None], [None], [None]]], 141: [27.10727, [[None], [None], [None]]], 142: [27.0487, [[None], [None], [None]]], 143: [27.00803, [[None], [None], [None]]], 144: [26.857, [[None], [None], [None]]], 145: [26.88322, [[None], [None], [None]]], 146: [26.95424, [[None], [None], [None]]], 147: [26.73751, [[None], [None], [None]]], 148: [26.75868, [[None], [None], [None]]], 149: [26.72847, [[None], [None], [None]]], 150: [26.75385, [[None], [None], [None]]], 151: [26.69617, [[None], [None], [None]]], 152: [26.68894, [[None], [None], [None]]], 153: [26.47, [[None], [None], [None]]], 154: [26.44925, [[None], [None], [None]]], 155: [26.46527, [[None], [None], [None]]], 156: [26.43105, [[None], [None], [None]]], 157: [26.38279, [[None], [None], [None]]], 158: [26.51412, [[None], [None], [None]]], 159: [26.02104, [[None], [None], [None]]], 160: [28.82675, [[None], [None], [None]]], 161: [29.14081, [[None], [None], [None]]], 162: [28.69929, [[None], [None], [None]]], 163: [28.72382, [[None], [None], [None]]], 164: [28.76485, [[None], [None], [None]]], 165: [28.69134, [[None], [None], [None]]], 166: [28.59944, [[None], [None], [None]]], 167: [28.6306, [[None], [None], [None]]], 168: [28.27344, [[None], [None], [None]]], 169: [28.34126, [[None], [None], [None]]], 170: [28.31084, [[None], [None], [None]]], 171: [28.29703, [[None], [None], [None]]], 172: [28.26908, [[None], [None], [None]]], 173: [28.1534, [[None], [None], [None]]], 174: [28.08121, [[None], [None], [None]]], 175: [28.00045, [[None], [None], [None]]], 176: [27.88412, [[None], [None], [None]]], 177: [27.83464, [[None], [None], [None]]], 178: [27.8603, [[None], [None], [None]]], 179: [27.63527, [[None], [None], [None]]], 180: [27.56742, [[None], [None], [None]]], 181: [27.47664, [[None], [None], [None]]], 182: [27.49684, [[None], [None], [None]]], 183: [27.35519, [[None], [None], [None]]], 184: [27.41958, [[None], [None], [None]]], 185: [27.48983, [[None], [None], [None]]], 186: [27.3427, [[None], [None], [None]]], 187: [27.38942, [[None], [None], [None]]], 188: [27.18689, 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[None]]], 1002: [61.87056, [[None], [None], [None]]], 1003: [62.32599, [[None], [None], [None]]], 1004: [61.81038, [[None], [None], [None]]], 1005: [61.32087, [[None], [None], [None]]], 1006: [62.06689, [[None], [None], [None]]], 1007: [62.12976, [[None], [None], [None]]], 1008: [61.94397, [[None], [None], [None]]], 1009: [60.34388, [[None], [None], [None]]], 1010: [61.20411, [[None], [None], [None]]], 1011: [61.6619, [[None], [None], [None]]], 1012: [62.89844, [[None], [None], [None]]], 1013: [62.84636, [[None], [None], [None]]], 1014: [62.75323, [[None], [None], [None]]], 1015: [62.76131, [[None], [None], [None]]], 1016: [62.29607, [[None], [None], [None]]], 1017: [62.95511, [[None], [None], [None]]], 1018: [62.37658, [[None], [None], [None]]], 1019: [62.25956, [[None], [None], [None]]], 1020: [64.2586, [[None], [None], [None]]], 1021: [64.09359, [[None], [None], [None]]], 1022: [63.94989, [[None], [None], [None]]], 1023: [62.68023, [[None], [None], [None]]], 1024: [64.51691, [[None], [None], [None]]], 1025: [65.14457, [[None], [None], [None]]], 1026: [65.06478, [[None], [None], [None]]], 1027: [64.81844, [[None], [None], [None]]], 1028: [65.44753, [[None], [None], [None]]], 1029: [65.12809, [[None], [None], [None]]], 1030: [65.63692, [[None], [None], [None]]], 1031: [23.43943, [[None], [None], [None]]], 1032: [23.88771, [[None], [None], [None]]], 1033: [23.76538, [[None], [None], [None]]], 1034: [23.62846, [[None], [None], [None]]], 1035: [23.9753, [[None], [None], [None]]], 1036: [23.84666, [[None], [None], [None]]], 1037: [24.63349, [[None], [None], [None]]], 1038: [24.14967, [[None], [None], [None]]], 1039: [23.74807, [[None], [None], [None]]], 1040: [23.65648, [[None], [None], [None]]], 1041: [23.66182, [[None], [None], [None]]], 1042: [23.88651, [[None], [None], [None]]], 1043: [23.67838, [[None], [None], [None]]], 1044: [23.81684, [[None], [None], [None]]], 1045: [25.59754, [[None], [None], [None]]], 1046: [24.59769, [[None], [None], [None]]], 1047: [24.03265, [[None], [None], [None]]], 1048: [24.09944, [[None], [None], [None]]], 1049: [23.49926, [[None], [None], [None]]], 1050: [26.42702, [[None], [None], [None]]], 1051: [25.81535, [[None], [None], [None]]], 1052: [24.06443, [[None], [None], [None]]], 1053: [23.90659, [[None], [None], [None]]], 1054: [24.09562, [[None], [None], [None]]], 1055: [24.07308, [[None], [None], [None]]], 1056: [24.11252, [[None], [None], [None]]], 1057: [24.09664, [[None], [None], [None]]], 1058: [24.0978, [[None], [None], [None]]], 1059: [23.83008, [[None], [None], [None]]], 1060: [24.27275, [[None], [None], [None]]], 1061: [24.26162, [[None], [None], [None]]]})
In [14]:
#retrieve the first item from each tuple in the OrderedDict
lats=[LAT_result.results[key][0] for key in LAT_result.results.keys()]
print(lats)
[23.88546, 24.13825, 24.1485, 24.27572, 24.24514, 23.91972, 23.946, 23.84638, 23.89042, 23.71201, 23.61503, 23.58447, 23.55528, 23.44287, 23.42304, 25.04014, 24.91874, 25.04765, 24.90684, 24.8422, 24.76182, 24.67565, 24.65291, 24.48346, 24.48123, 24.3513, 24.33485, 24.15196, 24.18112, 23.95482, 24.05741, 23.96709, 23.9349, 23.94473, 23.90483, 23.81682, 23.77813, 23.53743, 23.52625, 23.49772, 23.4699, 23.47914, 23.45317, 26.52033, 26.47708, 26.06959, 25.70784, 25.86563, 25.55626, 25.48875, 25.24214, 25.09217, 25.13091, 24.92424, 24.95574, 24.96049, 24.83573, 24.73502, 24.69981, 24.66988, 24.64407, 24.63757, 24.64224, 24.50298, 24.4413, 24.4286, 24.37277, 24.27918, 24.29192, 24.26057, 24.14908, 24.26765, 24.25114, 24.19405, 24.18801, 23.79131, 24.25591, 24.16476, 24.20943, 27.17957, 26.29766, 26.12948, 25.97338, 25.67264, 25.72037, 25.77709, 25.69529, 25.61722, 25.57785, 25.44323, 25.49211, 25.46377, 25.13669, 25.14887, 25.03423, 25.06267, 25.02271, 25.00869, 24.97182, 24.88576, 24.88535, 27.7165, 27.49357, 27.20799, 27.26525, 27.18259, 27.02128, 26.93676, 26.91403, 26.83589, 26.73431, 26.68701, 26.47433, 26.63596, 26.58517, 26.3882, 26.34006, 26.31421, 26.14542, 25.92634, 25.8818, 25.87666, 25.74154, 25.71231, 25.73976, 25.70528, 25.64259, 25.99144, 28.24348, 28.17146, 28.4863, 28.22952, 27.90356, 27.78588, 27.90596, 27.46361, 27.49238, 27.50379, 27.43305, 27.3743, 27.18386, 27.10727, 27.0487, 27.00803, 26.857, 26.88322, 26.95424, 26.73751, 26.75868, 26.72847, 26.75385, 26.69617, 26.68894, 26.47, 26.44925, 26.46527, 26.43105, 26.38279, 26.51412, 26.02104, 28.82675, 29.14081, 28.69929, 28.72382, 28.76485, 28.69134, 28.59944, 28.6306, 28.27344, 28.34126, 28.31084, 28.29703, 28.26908, 28.1534, 28.08121, 28.00045, 27.88412, 27.83464, 27.8603, 27.63527, 27.56742, 27.47664, 27.49684, 27.35519, 27.41958, 27.48983, 27.3427, 27.38942, 27.18689, 27.20614, 27.27453, 27.19126, 27.14429, 27.24766, 26.81265, 27.06744, 29.3933, 29.61962, 29.97135, 29.92867, 29.78977, 29.66592, 29.48307, 29.54292, 29.48138, 29.41644, 29.35769, 29.26727, 29.21343, 29.10013, 29.07135, 28.91521, 28.79105, 28.61927, 28.64932, 28.61893, 28.44549, 28.02238, 28.31338, 28.29904, 28.24076, 28.07201, 28.09458, 28.10002, 28.05613, 27.9546, 28.01244, 28.04241, 27.54365, 27.81313, 30.21793, 30.23904, 28.23445, 30.81361, 30.46667, 30.14375, 30.13366, 30.11592, 30.04341, 30.03201, 29.99009, 29.88434, 29.76598, 29.81042, 29.67547, 29.4231, 29.51026, 29.45563, 29.08158, 29.00889, 29.01575, 29.00973, 29.08299, 28.95621, 28.74362, 28.92169, 28.73176, 28.87099, 28.90016, 28.91695, 28.85077, 28.75352, 28.75595, 28.8071, 28.33161, 28.83348, 28.80196, 28.5016, 28.39676, 28.74696, 31.66718, 31.23367, 30.97539, 31.0601, 30.98977, 30.82581, 30.78237, 30.64289, 30.72949, 30.65805, 30.62624, 30.54737, 30.38086, 30.3032, 29.96456, 30.00471, 29.96785, 29.89746, 29.78395, 29.76021, 29.70734, 29.74419, 29.71406, 29.50902, 29.60397, 29.62631, 29.63378, 29.55402, 29.51251, 29.40216, 29.58772, 29.4772, 29.47761, 29.47814, 32.57842, 32.60322, 31.77553, 31.83354, 31.90285, 31.77077, 31.3633, 31.59006, 31.5593, 31.47769, 31.33225, 31.1576, 31.13598, 30.83458, 30.85587, 30.82207, 30.71629, 30.6898, 30.57831, 30.58585, 30.50646, 30.52071, 30.51523, 30.4975, 30.24442, 30.52596, 30.47029, 30.33047, 30.37663, 30.43777, 30.17062, 30.41196, 30.23965, 30.39518, 30.41521, 30.41972, 30.50164, 30.19096, 30.58568, 32.99646, 33.04918, 33.44492, 32.64611, 32.64618, 32.61877, 32.45645, 32.43644, 32.39627, 32.29597, 32.1414, 31.93598, 31.75472, 31.67363, 31.65635, 31.5713, 31.55154, 31.36047, 31.40735, 31.33668, 31.35645, 31.10342, 31.33965, 31.27045, 31.30933, 31.20925, 31.08327, 31.36174, 31.20907, 31.23731, 31.24628, 31.19546, 30.94593, 31.41025, 33.79646, 34.12951, 33.14087, 33.44195, 33.51212, 33.45157, 33.33897, 33.25274, 32.95111, 32.56404, 32.51542, 32.34206, 32.2846, 32.307, 32.11308, 31.84829, 31.98574, 32.13322, 32.1068, 32.07625, 32.12307, 32.08215, 32.1196, 32.05266, 32.10323, 32.01903, 32.04958, 31.90278, 31.5833, 31.57084, 34.47674, 34.34655, 34.31948, 34.18079, 33.99748, 33.90244, 33.62333, 33.53019, 33.36664, 33.26621, 33.20024, 33.13777, 33.11694, 33.1031, 33.37936, 32.8302, 32.81969, 32.98064, 32.96351, 32.85172, 32.91803, 33.0157, 32.80656, 32.9442, 32.7228, 32.43115, 32.92313, 32.36866, 36.27629, 35.8885, 35.54873, 35.89762, 35.11246, 35.20135, 35.06081, 35.12593, 34.73738, 34.62596, 34.57901, 34.4625, 34.30863, 34.28846, 34.18689, 33.97042, 33.96953, 33.89772, 33.85054, 33.79092, 33.82443, 33.82129, 33.85731, 33.88671, 33.84895, 33.83765, 33.8756, 33.83245, 33.87471, 33.74724, 33.68954, 33.89104, 33.71156, 33.87215, 34.06239, 35.87512, 35.9704, 35.892, 35.74419, 35.73424, 35.68845, 35.48928, 35.4775, 35.44466, 35.30646, 35.24543, 35.21891, 34.96942, 34.731, 34.80394, 35.07484, 35.05823, 34.99983, 34.68054, 34.62495, 34.74625, 34.72877, 34.63443, 34.70542, 34.74907, 34.61156, 34.64944, 34.97136, 38.14257, 37.01234, 36.82486, 36.51848, 36.10048, 36.03143, 35.96591, 36.18066, 36.13855, 36.00199, 35.91249, 35.88386, 35.87833, 35.67868, 35.6613, 35.57346, 35.65461, 35.66388, 35.48149, 35.4394, 35.45509, 35.74015, 35.73111, 38.99419, 38.79726, 37.87952, 37.42159, 37.19012, 37.03434, 37.12876, 37.05673, 36.90378, 36.85841, 36.79677, 36.56797, 36.5301, 36.28593, 36.54959, 38.08536, 36.7364, 39.75614, 39.54709, 39.53364, 37.79984, 37.63493, 38.46239, 38.16826, 38.11568, 38.095, 37.96344, 37.78398, 37.7181, 37.09305, 37.53058, 37.30994, 37.63197, 37.5846, 37.20987, 38.10533, 37.71206, 40.36892, 40.29509, 39.74994, 39.03273, 39.39682, 39.50991, 38.72956, 39.0426, 39.09142, 39.17496, 39.07996, 39.00847, 38.86933, 38.7798, 38.72679, 38.66412, 38.29753, 38.32938, 38.30407, 37.74867, 38.16866, 38.20821, 38.24185, 38.7911, 38.70135, 40.70103, 41.50675, 40.9163, 40.93557, 40.64417, 40.32453, 40.56974, 40.43657, 39.51928, 40.17767, 40.11176, 40.0102, 39.87011, 39.87516, 39.3946, 39.63554, 39.62582, 39.54827, 39.57608, 39.51914, 39.30276, 39.36804, 39.2848, 39.16183, 39.32154, 39.24207, 39.29208, 38.93855, 39.73428, 41.82072, 42.48715, 41.84007, 41.54676, 40.8245, 41.03525, 40.95813, 40.68306, 40.64183, 41.17271, 41.16909, 41.03978, 40.93634, 40.8481, 40.83451, 40.17989, 40.53118, 40.46893, 40.38719, 40.29251, 40.31708, 40.23475, 40.02974, 40.06276, 40.26149, 39.87035, 39.60819, 39.82859, 42.83702, 42.93719, 42.66483, 43.35178, 42.49542, 42.528, 42.2462, 41.72824, 42.12786, 41.98991, 42.00594, 41.60387, 41.35955, 41.37909, 41.21978, 41.29752, 41.30604, 41.33801, 41.52016, 41.16064, 41.24084, 43.73039, 43.43611, 41.58644, 44.78891, 44.92809, 44.15172, 44.44107, 43.127, 43.53328, 43.35417, 42.93962, 43.42665, 43.01173, 43.2329, 43.16596, 43.00381, 43.00834, 42.66726, 42.54007, 42.4402, 42.23752, 42.23689, 42.21948, 42.36347, 41.97925, 41.71844, 41.92517, 42.17039, 41.86826, 42.12193, 46.3446, 45.93179, 44.63229, 45.04854, 45.32946, 45.25527, 44.77826, 44.31297, 44.34708, 44.55629, 44.45748, 44.22736, 44.05649, 44.07836, 43.61903, 43.54108, 43.10127, 43.25125, 43.40799, 43.51183, 43.46817, 42.74399, 42.74895, 43.08078, 43.3651, 43.55388, 43.36382, 43.04593, 43.1264, 46.3997, 46.54007, 46.36334, 46.81459, 46.14459, 46.4832, 46.15767, 46.38921, 45.79562, 45.37233, 45.45887, 45.62742, 45.59382, 45.46169, 44.78307, 44.6977, 44.03256, 44.07016, 44.38736, 44.48754, 44.2782, 44.16013, 44.67494, 44.14917, 43.77567, 47.10633, 47.54764, 47.76531, 48.03157, 47.18156, 47.88243, 47.55265, 46.90437, 46.6834, 46.23235, 46.59868, 46.50368, 46.85554, 46.70583, 46.55841, 46.64879, 45.64355, 45.94243, 45.75569, 45.82293, 45.80528, 45.7929, 45.83224, 45.13094, 45.36867, 45.69789, 45.55158, 45.35452, 45.34604, 45.18947, 45.62072, 46.98153, 48.93346, 48.72341, 48.59163, 47.9297, 48.42228, 48.52363, 48.37927, 48.05177, 47.44342, 47.03814, 47.12463, 47.87564, 47.4746, 47.10721, 46.79322, 47.29402, 47.28373, 47.20168, 47.00249, 46.85698, 46.31955, 47.10663, 46.02328, 46.38274, 46.45124, 46.42673, 45.91271, 45.90528, 49.80675, 50.16524, 50.77127, 50.28334, 49.92423, 49.5792, 49.94552, 49.4731, 48.45442, 48.51238, 48.21277, 48.21436, 47.98339, 48.22277, 48.21589, 47.38223, 48.00664, 48.40556, 48.28071, 48.17834, 47.97032, 48.17979, 48.00316, 47.00244, 47.48919, 47.41957, 47.96225, 48.06156, 47.90073, 48.56264, 48.76056, 51.81247, 50.93649, 50.5205, 51.19989, 49.7491, 49.2383, 49.71121, 49.0078, 49.02048, 49.60175, 49.06337, 49.37324, 48.6421, 49.5529, 49.035, 48.68116, 49.34415, 48.7683, 48.44514, 48.82634, 48.52672, 48.855, 51.35375, 50.3049, 49.56234, 49.49344, 49.47329, 52.16626, 52.40632, 52.31464, 51.10774, 51.49604, 50.21281, 51.06818, 51.03817, 50.19053, 49.89084, 50.47361, 49.74231, 50.26022, 50.06728, 50.28843, 49.81754, 50.34027, 49.95812, 50.33751, 52.62537, 50.66044, 50.79457, 53.09803, 52.65538, 53.0754, 53.36749, 52.9915, 53.21471, 52.91048, 51.67044, 52.37521, 51.12273, 51.02727, 51.20628, 51.22747, 51.0971, 51.18864, 50.9467, 50.78048, 51.81538, 51.24165, 51.78825, 54.00426, 54.24141, 54.85473, 54.60972, 53.64296, 54.3993, 53.37854, 54.48213, 54.37729, 53.11539, 53.42715, 53.03588, 53.72374, 53.30414, 53.45566, 53.5949, 53.58461, 52.95049, 52.95736, 53.00519, 53.19355, 52.87213, 55.7192, 56.19957, 56.66246, 55.50722, 56.22749, 56.34102, 54.75339, 56.10592, 55.28068, 55.64146, 54.57986, 54.94292, 55.18671, 54.23066, 54.52189, 53.86779, 54.79068, 53.53877, 54.16527, 54.1458, 56.97961, 57.04969, 57.42005, 57.58266, 57.70623, 57.10131, 57.25885, 56.19167, 56.44063, 56.62924, 56.09536, 55.48492, 55.67151, 56.37594, 56.62345, 56.48011, 55.8236, 56.30722, 56.31218, 56.3691, 56.00103, 57.73428, 58.31187, 58.60666, 58.45903, 58.14334, 58.44059, 58.46813, 59.00069, 57.34809, 57.48598, 57.22813, 57.38189, 58.10778, 56.68025, 56.72209, 56.44014, 56.6474, 56.48177, 57.00126, 57.15744, 59.80535, 60.21158, 60.23591, 60.08923, 60.35629, 60.98851, 60.87529, 60.60532, 59.84943, 60.14177, 61.55154, 61.19527, 61.28703, 61.74811, 61.87056, 62.32599, 61.81038, 61.32087, 62.06689, 62.12976, 61.94397, 60.34388, 61.20411, 61.6619, 62.89844, 62.84636, 62.75323, 62.76131, 62.29607, 62.95511, 62.37658, 62.25956, 64.2586, 64.09359, 63.94989, 62.68023, 64.51691, 65.14457, 65.06478, 64.81844, 65.44753, 65.12809, 65.63692, 23.43943, 23.88771, 23.76538, 23.62846, 23.9753, 23.84666, 24.63349, 24.14967, 23.74807, 23.65648, 23.66182, 23.88651, 23.67838, 23.81684, 25.59754, 24.59769, 24.03265, 24.09944, 23.49926, 26.42702, 25.81535, 24.06443, 23.90659, 24.09562, 24.07308, 24.11252, 24.09664, 24.0978, 23.83008, 24.27275, 24.26162]
In [15]:
# Get the number of records for this message
num_records = len(lats)
print(num_records)
1062
In [16]:
#longitude
LON_result = DataQuerent(NodePathParser()).query(bufr_message, '006001')
#uncomment the line below to see the results
#print(LON_result.results)
In [17]:
lons=[LON_result.results[key][0] for key in LON_result.results.keys()]
#print(lons)
In [18]:
#Wind_U
WINDU_result = DataQuerent(NodePathParser()).query(bufr_message, '011003')
#uncomment the line below to see the results
#print(WINDU_result.results)
In [19]:
windus=[WINDU_result.results[key][0] for key in WINDU_result.results.keys()]
#uncomment the line below to see the results
#print(windus)
In [20]:
#Wind_V
WINDV_result = DataQuerent(NodePathParser()).query(bufr_message, '011004')
#uncomment the line below to see the results
#print(WINDV.results)
In [21]:
windvs=[WINDV_result.results[key][0] for key in WINDV_result.results.keys()]
In [22]:
#pressure
PRESSURE_result = DataQuerent(NodePathParser()).query(bufr_message, '007004')
#print(PRESSURE_result.results)
In [23]:
press=[PRESSURE_result.results[key][0] for key in PRESSURE_result.results.keys()]
#print(press)
Now conduct the import of all required parameters (lat, lon, windsv, windsu and press) in a singe loop¶
In [24]:
#retrieve various BUFR parameters, for all messages in a single loop
lats = np.array([])
lons = np.array([])
windvs = np.array([])
windus = np.array([])
press = np.array([])
for message in messages:
LAT_result = DataQuerent(NodePathParser()).query(message, '005001')
lats=np.concatenate((lats, np.array([LAT_result.results[key][0] for key in LAT_result.results.keys()])))
LON_result = DataQuerent(NodePathParser()).query(message, '006001')
lons=np.concatenate((lons,np.array([LON_result.results[key][0] for key in LON_result.results.keys()])))
WINDV_result = DataQuerent(NodePathParser()).query(message, str('011004'))
windvs=np.concatenate((windvs,np.array([WINDV_result.results[key][0] for key in WINDV_result.results.keys()])))
WINDU_result = DataQuerent(NodePathParser()).query(message, str('011003'))
windus=np.concatenate((windus,np.array([WINDU_result.results[key][0] for key in WINDU_result.results.keys()])))
PRESSURE_result = DataQuerent(NodePathParser()).query(message, str('007004'))
press=np.concatenate((press,np.array([PRESSURE_result.results[key][0] for key in PRESSURE_result.results.keys()])))
In [25]:
# Get the number of records for all messages
num_records = len(lats)
print(num_records)
52102
In [26]:
#to compare with results of individual parameter extraction uncomment the line below
#print(press)
In [27]:
# Scatter plot of pressure data
vals = press # your pressure values
# Create normalized color values
norm = mcolors.Normalize(vmin=min(vals), vmax=max(vals))
colormap = cm.rainbow
plt.figure(figsize=(6,6))
# Plot scatter with colormap
sc = plt.scatter(lons, lats, c=vals, cmap=colormap, norm=norm, marker='.')
# Add colorbar as legend
cbar = plt.colorbar(sc, fraction=0.0235, pad=0.03, shrink=0.5)
cbar.set_label('Pressure - Pa') # Label for the colorbar
plt.xlabel('Longitude')
plt.ylabel('Latitude')
plt.title('Pressure')
plt.xlim(-67.5, 67.5)
plt.ylim(-67.5, 67.5)
plt.show()
In [28]:
# convert numpy array to pandas dataframe and add 3 more columns (WINDSP, X and Y)
dataset = pd.DataFrame({'LAT': lats, 'LON': lons, 'WINDV': windvs, 'WINDU': windus, 'WINDSP':np.nan, 'PRESSURE':press, 'X':np.nan, 'Y':np.nan})
dataset
Out[28]:
| LAT | LON | WINDV | WINDU | WINDSP | PRESSURE | X | Y | |
|---|---|---|---|---|---|---|---|---|
| 0 | 23.40330 | -23.80453 | -7.6 | -6.7 | NaN | 90000.0 | NaN | NaN |
| 1 | 23.40835 | -24.48511 | -8.8 | -6.8 | NaN | 90000.0 | NaN | NaN |
| 2 | 23.43592 | -25.63245 | -6.9 | -7.2 | NaN | 89990.0 | NaN | NaN |
| 3 | 23.42481 | -25.80081 | -6.5 | -7.1 | NaN | 90000.0 | NaN | NaN |
| 4 | 23.61610 | -29.52743 | -7.4 | -6.3 | NaN | 97460.0 | NaN | NaN |
| ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 52097 | 24.09664 | 7.63630 | -10.7 | -4.5 | NaN | 45640.0 | NaN | NaN |
| 52098 | 24.09780 | 6.38153 | -4.0 | -8.2 | NaN | 44190.0 | NaN | NaN |
| 52099 | 23.83008 | 5.54048 | -0.7 | -9.2 | NaN | 45330.0 | NaN | NaN |
| 52100 | 24.27275 | 5.17012 | -2.2 | -10.6 | NaN | 54850.0 | NaN | NaN |
| 52101 | 24.26162 | 4.62287 | -2.1 | -12.7 | NaN | 59410.0 | NaN | NaN |
52102 rows × 8 columns
If wind speed is given as eastward and northward components (u and v), the overall wind speed (ws) can be calculated using the Pythagorean theorem: ws = √(u² + v²).
In [29]:
#Calculate the Windspeed
dataset['WINDSP'] = np.sqrt(dataset['WINDV']**2+dataset['WINDV']**2)
dataset
Out[29]:
| LAT | LON | WINDV | WINDU | WINDSP | PRESSURE | X | Y | |
|---|---|---|---|---|---|---|---|---|
| 0 | 23.40330 | -23.80453 | -7.6 | -6.7 | 10.748023 | 90000.0 | NaN | NaN |
| 1 | 23.40835 | -24.48511 | -8.8 | -6.8 | 12.445079 | 90000.0 | NaN | NaN |
| 2 | 23.43592 | -25.63245 | -6.9 | -7.2 | 9.758074 | 89990.0 | NaN | NaN |
| 3 | 23.42481 | -25.80081 | -6.5 | -7.1 | 9.192388 | 90000.0 | NaN | NaN |
| 4 | 23.61610 | -29.52743 | -7.4 | -6.3 | 10.465180 | 97460.0 | NaN | NaN |
| ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 52097 | 24.09664 | 7.63630 | -10.7 | -4.5 | 15.132085 | 45640.0 | NaN | NaN |
| 52098 | 24.09780 | 6.38153 | -4.0 | -8.2 | 5.656854 | 44190.0 | NaN | NaN |
| 52099 | 23.83008 | 5.54048 | -0.7 | -9.2 | 0.989949 | 45330.0 | NaN | NaN |
| 52100 | 24.27275 | 5.17012 | -2.2 | -10.6 | 3.111270 | 54850.0 | NaN | NaN |
| 52101 | 24.26162 | 4.62287 | -2.1 | -12.7 | 2.969848 | 59410.0 | NaN | NaN |
52102 rows × 8 columns
In [30]:
#save the preliminary results as a csv file
dataset.to_csv(work_dir+'/AMV_bufr_out.csv')
In [31]:
#check your folder and note the additional messages created
#remove obsolete files, here the seperate messages extracted
for p in glob(work_dir+'/*.bin.*', recursive=True):
if os.path.isfile(p):
os.remove(p)
Changing projection - visualization¶
In [32]:
#retreive the latitude and longitude coordinates (per pixel - row) and transform these into Meteosat Geostationary 'GEOS' projection X and Y
csyLL = ilwis.CoordinateSystem('epsg:4326') #define latlon to be used as input
csyGeos = ilwis.CoordinateSystem('code=proj4:+proj=geos +h=35785831 +a=6378137 +rf=298.257223563')#define GEOS - geostationary projection to be used as output
#conduct the transformation
for index, row in dataset.iterrows():
crdGeos = csyGeos.coord2coord(csyLL, ilwis.Coordinate(row['LON'],row['LAT']))
dataset.loc[index, 'X'] = crdGeos.x
dataset.loc[index, 'Y'] = crdGeos.y
dataset
Out[32]:
| LAT | LON | WINDV | WINDU | WINDSP | PRESSURE | X | Y | |
|---|---|---|---|---|---|---|---|---|
| 0 | 23.40330 | -23.80453 | -7.6 | -6.7 | 10.748023 | 90000.0 | -2.295141e+06 | 2.439189e+06 |
| 1 | 23.40835 | -24.48511 | -8.8 | -6.8 | 12.445079 | 90000.0 | -2.354684e+06 | 2.437529e+06 |
| 2 | 23.43592 | -25.63245 | -6.9 | -7.2 | 9.758074 | 89990.0 | -2.453547e+06 | 2.436433e+06 |
| 3 | 23.42481 | -25.80081 | -6.5 | -7.1 | 9.192388 | 90000.0 | -2.468252e+06 | 2.434814e+06 |
| 4 | 23.61610 | -29.52743 | -7.4 | -6.3 | 10.465180 | 97460.0 | -2.775813e+06 | 2.439842e+06 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 52097 | 24.09664 | 7.63630 | -10.7 | -4.5 | 15.132085 | 45640.0 | 7.611608e+05 | 2.540235e+06 |
| 52098 | 24.09780 | 6.38153 | -4.0 | -8.2 | 5.656854 | 44190.0 | 6.369530e+05 | 2.541602e+06 |
| 52099 | 23.83008 | 5.54048 | -0.7 | -9.2 | 0.989949 | 45330.0 | 5.547642e+05 | 2.516616e+06 |
| 52100 | 24.27275 | 5.17012 | -2.2 | -10.6 | 3.111270 | 54850.0 | 5.157855e+05 | 2.559355e+06 |
| 52101 | 24.26162 | 4.62287 | -2.1 | -12.7 | 2.969848 | 59410.0 | 4.614290e+05 | 2.558676e+06 |
52102 rows × 8 columns
In [33]:
#get minimum and maximum value of a parameter
PARAM_Min = (dataset['PRESSURE'].min())
PARAM_Max = (dataset['PRESSURE'].max())
print(PARAM_Min)
print(PARAM_Max)
12350.0 102200.0
In [34]:
#resample the point data set to a raster given an aggregation factor
# Set aggregation Factor - See https://user.eumetsat.int/s3/eup-strapi-media/pdf_msg_met_prod_atbd_15e4917e25.pdf page 28
aggregate_factor = 32 # Can be 1, 3, 16 or 32
In [35]:
#mapping of selected parameter
x_size = int(3712 / aggregate_factor)
y_size = int(3712 / aggregate_factor)
xmin = -5568748.28
ymin = -5568748.28
xmax = 5568748.28
ymax = 5568748.28
pixel_size_x = (xmax - xmin) / x_size
pixel_size_y = (ymax - ymin) / y_size
print(x_size, y_size, pixel_size_x, pixel_size_y)
data = np.full((y_size, x_size), -9999, dtype=np.float64)
count = np.full((y_size, x_size), 0, dtype=np.float64)
#retrieve the mean data value of all initial values within the new raster resolution cell. If more coordinate pairs fall within the same pixel
#the average value is computed (using the count). This is only done for those pixels not having the value of -9999
for index, row in dataset.iterrows():
xpos = int((row['X'] - xmin) / pixel_size_x)
ypos = y_size - int((row['Y'] - ymin) / pixel_size_y) - 1
data[ypos,xpos] = row['PRESSURE'] if data[ypos,xpos] == -9999 else data[ypos,xpos] + row['PRESSURE']
count[ypos,xpos] += 1
data[count>0] /= count[count>0] # calculate the average for the new pixel using the number of counts
116 116 96012.90137931035 96012.90137931035
In [36]:
#new shape - raster dimensions
print(np.shape(data))
(116, 116)
In [37]:
#create a new ilwis raster
grf = ilwis.GeoReference('code=georef:type=corners,csy=proj4:+proj=geos +h=35785831 +a=6378137 +rf=298.257223563,envelope='+ str(xmin) + ' ' + str(ymax) + ' ' + str(xmax) + ' ' + str(ymin) + ',gridsize=' + str(x_size) + ' ' + str(y_size) + ',cornerofcorners=yes')
dfNum = ilwis.DataDefinition(ilwis.NumericDomain('code=value'), ilwis.NumericRange(PARAM_Min, PARAM_Max, 0))
rcNew = ilwis.RasterCoverage()
rcNew.setSize(ilwis.Size(x_size,y_size,1))
rcNew.setGeoReference(grf)
rcNew.setDataDef(dfNum)
In [38]:
#fill the new raster with the data calulated above
rcNew.array2raster(data.flatten())
print(rcNew.size())
Size(116, 116, 1)
In [39]:
#for all pixels with no data set the value to -9999
rcNew1 = ilwis.do('mapcalc', 'iff(@1==?,-9999, @1)',rcNew)
In [40]:
#create a new numpy array
p_2np = np.fromiter(iter(rcNew1), np.float64, rcNew1.size().linearSize())
p_2np = p_2np.reshape((rcNew1.size().ysize, rcNew1.size().xsize))
In [41]:
#mask the array using the value of -9999
p_maskarray = np.ma.masked_where(p_2np == -9999, p_2np)
#p_maskarray.shape
In [42]:
#plot the dataset using imshow - note the coordinates (metric - geostationary - origin is in the display centre!)
cf = plt.imshow(p_maskarray/100, extent=[xmin, xmax, ymin, ymax], origin='upper', vmin=PARAM_Min/100, vmax=PARAM_Max/100, interpolation = 'None', cmap='jet')
cb = plt.colorbar(cf, fraction=0.0235, pad=0.03, shrink=0.6 )
cb.set_label('hPa', fontsize=15)
plt.xlabel('X Coordinate (meters)')
plt.ylabel('Y Coordinate (meters)')
plt.title('Aggregated (average) Pressure Map')
plt.show()
In [43]:
#store resulting map - check in ilwis386
rcNew1.store('pressure.mpr')
In [44]:
#transform raster created above to new Geographic lat-lon coordinates
#create target georeference
#note new window selected envelope=-10 -15 50 -35 (southern hemisphere), with a different gridsize=600 200,
column_size = 600
row_size = 200
lat_min = -35
lat_max = -10
lon_min = -10
lon_max = 60
grfTarget = ilwis.GeoReference('code=georef:type=corners, csy=epsg:4326, envelope='+ str(lon_min) + ' ' + str(lat_max) + ' ' + str(lon_max) + ' ' + str(lat_min) + ',gridsize=' + str(row_size) + ' ' + str(column_size) + ',cornerofcorners=yes')
In [45]:
#conduct the resampling operation
rc_res = ilwis.do('resample', rcNew1, grfTarget,'nearestneighbour')#'bilinear')
In [46]:
#transform to numpy array
p_2np = np.fromiter(iter(rc_res), np.float64, rc_res.size().linearSize())
p_2np = p_2np.reshape((rc_res.size().ysize, rc_res.size().xsize))
In [47]:
#review the shape of the new 2D array
p_2np.shape
Out[47]:
(600, 200)
In [48]:
#mask the no-data
p_maskedarray = np.ma.masked_where(p_2np == -9999, p_2np)
In [49]:
#plot the figure - 2 D array - note pressure is now in hPa
plt.figure(figsize=(8,6))
cf = plt.imshow(p_maskedarray/100, extent=[lon_min, lon_max, lat_min, lat_max], origin='upper', vmin=PARAM_Min/100, vmax=PARAM_Max/100, interpolation = 'None', cmap='jet')
cb = plt.colorbar(cf, fraction=0.0235, pad=0.03, shrink=0.4 )
cb.set_label('hPa', fontsize=15)
plt.title('Pressure')
plt.xlabel('Longitude')
plt.ylabel('Latitude')
plt.show()
In [50]:
#store resulting map
rc_res.store('pressure_LL.mpr')
In [51]:
#create a new visualization using the wind direction (as arrows) and windspeed (as background map)
#Use quiver -see: https://matplotlib.org/stable/gallery/images_contours_and_fields/quiver_simple_demo.html
#Use Matplotlib Basemap - see: https://matplotlib.org/basemap/stable/
plt.figure(figsize=(12,10))
img_extent = (-10, 50, -35, -15)
colvals = preprocessing.minmax_scale(vals)
#plt.scatter(lons, lats, color=cm.rainbow(colvals), marker='.')
Q = plt.quiver(lons[::10],lats[::10],windus[::10],windvs[::10], scale_units='xy', scale=6, width=0.0015,color ='black')
cf = plt.imshow(p_maskedarray/100, extent=img_extent, vmin=PARAM_Min/100, vmax=PARAM_Max/100, interpolation = 'None', cmap='turbo')
cb = plt.colorbar(cf, fraction=0.0235, pad=0.03, shrink=0.35 )
cb.set_label('hPa', fontsize=10)
m = Basemap(projection='cyl', llcrnrlon=-10, llcrnrlat=-35, urcrnrlon=50, urcrnrlat=-15, resolution='i')
m.drawcoastlines(1)
m.drawcountries()
parallels = np.arange(-35,-15+0.25,10)
m.drawparallels(parallels, labels=[1,0,0,0], linewidth=0.5);
meridians = np.arange(-10,50+0.25,10)
m.drawmeridians(meridians, labels=[0,0,0,1], linewidth=0.5);
plt.show()
In [52]:
#create a new visualization using the wind direction (as barbs) and windspeed (as background map)
#see also: https://pilotinstitute.com/surface-analysis-charts-explained/#wind
#matplotlib barbs example - see: https://matplotlib.org/stable/gallery/images_contours_and_fields/barb_demo.html
#Use Matplotlib Basemap - see: https://matplotlib.org/basemap/stable/
plt.figure(figsize=(16,10))
# Default parameters
#plt.barbs(lons[::1],lats[::1],windus[::1],windvs[::1])
#plot every 3rd element
#don't round the values
#don't show empty barbs
plt.barbs(lons[::3],lats[::3],windus[::3],windvs[::3],np.sqrt(windus[::3] ** 2 + windvs[::3] ** 2), fill_empty=False, rounding=False,
sizes=dict(emptybarb=0.0, spacing=0.2, height=0.5), pivot='middle', cmap='turbo', zorder=3)
m = Basemap(projection='cyl', llcrnrlon=-10, llcrnrlat=-35, urcrnrlon=50, urcrnrlat=-15, resolution='i')
m.drawcoastlines(0.5)
m.drawcountries()
#note different background options
#m.drawmapboundary(fill_color='aqua')
#m.fillcontinents(color='yellow', lake_color='aqua')
#m.shadedrelief()
m.etopo(alpha =0.5) #set transparency
parallels = np.arange(-35,-15+0.25,10)
m.drawparallels(parallels, labels=[1,0,0,0], linewidth=0.5);
meridians = np.arange(-10,50+0.25,10)
m.drawmeridians(meridians, labels=[0,0,0,1], linewidth=0.5);
plt.show()
Retrieve all atmospheric motion vectors at pressure levels below a certain threshold¶
In [53]:
#enter threshold pressure value in Pascal
threshold = 70000 #700 hPa
In [54]:
dataset['HP'] = dataset['PRESSURE'].apply(lambda x: x if x <= threshold else np.nan)
dataset['HP'] = dataset['PRESSURE']/100
#Pressure unit is Pascal
dataset['HWV'] = dataset['WINDV'].where(dataset['PRESSURE'] <= threshold, np.nan)
dataset['HWU'] = dataset['WINDU'].where(dataset['PRESSURE'] <= threshold, np.nan)
In [55]:
#check the selected records
dataset
Out[55]:
| LAT | LON | WINDV | WINDU | WINDSP | PRESSURE | X | Y | HP | HWV | HWU | |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 23.40330 | -23.80453 | -7.6 | -6.7 | 10.748023 | 90000.0 | -2.295141e+06 | 2.439189e+06 | 900.0 | NaN | NaN |
| 1 | 23.40835 | -24.48511 | -8.8 | -6.8 | 12.445079 | 90000.0 | -2.354684e+06 | 2.437529e+06 | 900.0 | NaN | NaN |
| 2 | 23.43592 | -25.63245 | -6.9 | -7.2 | 9.758074 | 89990.0 | -2.453547e+06 | 2.436433e+06 | 899.9 | NaN | NaN |
| 3 | 23.42481 | -25.80081 | -6.5 | -7.1 | 9.192388 | 90000.0 | -2.468252e+06 | 2.434814e+06 | 900.0 | NaN | NaN |
| 4 | 23.61610 | -29.52743 | -7.4 | -6.3 | 10.465180 | 97460.0 | -2.775813e+06 | 2.439842e+06 | 974.6 | NaN | NaN |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 52097 | 24.09664 | 7.63630 | -10.7 | -4.5 | 15.132085 | 45640.0 | 7.611608e+05 | 2.540235e+06 | 456.4 | -10.7 | -4.5 |
| 52098 | 24.09780 | 6.38153 | -4.0 | -8.2 | 5.656854 | 44190.0 | 6.369530e+05 | 2.541602e+06 | 441.9 | -4.0 | -8.2 |
| 52099 | 23.83008 | 5.54048 | -0.7 | -9.2 | 0.989949 | 45330.0 | 5.547642e+05 | 2.516616e+06 | 453.3 | -0.7 | -9.2 |
| 52100 | 24.27275 | 5.17012 | -2.2 | -10.6 | 3.111270 | 54850.0 | 5.157855e+05 | 2.559355e+06 | 548.5 | -2.2 | -10.6 |
| 52101 | 24.26162 | 4.62287 | -2.1 | -12.7 | 2.969848 | 59410.0 | 4.614290e+05 | 2.558676e+06 | 594.1 | -2.1 | -12.7 |
52102 rows × 11 columns
In [56]:
hwv = dataset['HWV']
hwu = dataset['HWU']
In [57]:
#matplotlib barbs example - see: https://matplotlib.org/stable/gallery/images_contours_and_fields/barb_demo.html
#Use Matplotlib Basemap - see: https://matplotlib.org/basemap/stable/
plt.figure(figsize=(16,10))
# Default parameters
#plt.barbs(lons[::1],lats[::1],windus[::1],windvs[::1])
#plot every element selected
#don't round the values
#don't show empty barbs
plt.barbs(lons[::1],lats[::1],hwu[::1],hwv[::1],np.sqrt(hwu[::1] ** 2 + hwv[::1] ** 2), fill_empty=False, rounding=False,
sizes=dict(emptybarb=0.0, spacing=0.2, height=0.5), pivot='middle', cmap='turbo', zorder=3)
m = Basemap(projection='cyl', llcrnrlon=-10, llcrnrlat=-35, urcrnrlon=50, urcrnrlat=-15, resolution='i')
m.drawcoastlines(0.5)
m.drawcountries()
#note different background options
#m.drawmapboundary(fill_color='aqua')
#m.fillcontinents(color='yellow', lake_color='aqua')
#m.shadedrelief()
m.etopo(alpha =0.5) #set transparency
parallels = np.arange(-35,-15+0.25,10)
m.drawparallels(parallels, labels=[1,0,0,0], linewidth=0.5);
meridians = np.arange(-10,50+0.25,10)
m.drawmeridians(meridians, labels=[0,0,0,1], linewidth=0.5);
plt.show()
In [58]:
#Example code to experiment with the wind visualization parameters
#reference: https://matplotlib.org/stable/gallery/images_contours_and_fields/barb_demo.html
x = np.linspace(-5, 5, 5)
X, Y = np.meshgrid(x, x)
U, V = 12 * X, 12 * Y
data = [(-1.5, .5, -6, -6),
(1, -1, -46, 46),
(-3, -1, 11, -11),
(1, 1.5, 80, 80),
(0.5, 0.25, 25, 15),
(-1.5, -0.5, -5, 40)]
data = np.array(data, dtype=[('x', np.float32), ('y', np.float32),
('u', np.float32), ('v', np.float32)])
fig1, axs1 = plt.subplots(nrows=2, ncols=2)
# Default parameters, uniform grid
axs1[0, 0].barbs(X, Y, U, V)
# Arbitrary set of vectors, make them longer and change the pivot point
# (point around which they're rotated) to be the middle
axs1[0, 1].barbs(
data['x'], data['y'], data['u'], data['v'], length=8, pivot='middle')
# Showing colormapping with uniform grid. Fill the circle for an empty barb,
# don't round the values, and change some of the size parameters
axs1[1, 0].barbs(
X, Y, U, V, np.sqrt(U ** 2 + V ** 2), fill_empty=True, rounding=False,
sizes=dict(emptybarb=0.25, spacing=0.2, height=0.3))
# Change colors as well as the increments for parts of the barbs
axs1[1, 1].barbs(data['x'], data['y'], data['u'], data['v'], flagcolor='r',
barbcolor=['b', 'g'], flip_barb=True,
barb_increments=dict(half=10, full=20, flag=100))
# Masked arrays are also supported
masked_u = np.ma.masked_array(data['u'])
masked_u[4] = 1000 # Bad value that should not be plotted when masked
masked_u[4] = np.ma.masked
Processing global instabillity index bufr data: the K-Index¶
See for additional product information: https://navigator.eumetsat.int/product/EO:EUM:DAT:0328?query=GII%20msg%200%20&s=advanced
In [59]:
# Decode a AMV BUFR file
# BUFR input file
file_in = work_dir+r'/W_XX-EUMETSAT-Darmstadt,SING+LEV+SAT,MET10+GII_C_EUMG_20240612111500_3.bin'
In [60]:
#split all messages contained in BUFR file into seperate messages
subprocess.run(['pybufrkit', 'split', file_in])
Out[60]:
CompletedProcess(args=['pybufrkit', 'split', 'd:\\jupyter\\notebook_scripts\\ilwispy_tutorial\\tutorial_release/BUFR_GRIB_data/W_XX-EUMETSAT-Darmstadt,SING+LEV+SAT,MET10+GII_C_EUMG_20240612111500_3.bin'], returncode=0)
In [61]:
#read all messages and append
messages = []
decoder = Decoder()
path = file_in
filelist = glob(path + '.*')
for file in filelist:
with open((file), 'rb') as ins:
bufr_message = decoder.process(ins.read())
messages.append(bufr_message)
In [62]:
# Query the metadata
n_subsets = MetadataQuerent(MetadataExprParser()).query(bufr_message, '%n_subsets')
print(n_subsets)
14171
In [63]:
# Decode only the metadata sections of a BUFR file
result1 = subprocess.run(['pybufrkit', 'info', file_in], capture_output=True)
result1 = result1.stdout.decode('utf-8')
print(result1)
TableGroupKey(tables_root_dir='C:\\python313\\Lib\\site-packages\\pybufrkit\\tables', tables_local_dir='C:\\python313\\Lib\\site-packages\\pybufrkit\\tables', wmo_tables_sn=('0', '0_0', '12'), local_tables_sn=None)
<<<<<< section 0 >>>>>>
start_signature = b'BUFR'
length = 249954
edition = 4
<<<<<< section 1 >>>>>>
section_length = 22
master_table_number = 0
originating_centre = 254
originating_subcentre = 0
update_sequence_number = 0
is_section2_presents = False
flag_bits = '0000000'
data_category = 5
data_i18n_subcategory = 255
data_local_subcategory = 70
master_table_version = 12
local_table_version = 0
year = 2024
month = 6
day = 12
hour = 11
minute = 15
second = 0
local_bytes = b''
<<<<<< section 3 >>>>>>
section_length = 79
reserved_bits = '00000000'
n_subsets = 13973
is_observation = True
is_compressed = True
flag_bits = '000000'
unexpanded_descriptors = [301071, 301011, 301013, 5031, 6031, 301021, 7024, 13044, 13045, 201129, 13042, 201000, 13046, 13016, 33007, 7004, 7004, 201134, 202130, 13016, 202000, 201000, 7004, 7004, 201134, 202130, 13016, 202000, 201000, 7004, 7004, 201134, 202130, 13016, 202000, 201000]
<<<<<< section 4 >>>>>>
section_length = 249841
reserved_bits = '00000000'
In [64]:
#get further information about all non expanded_descriptors, first create a listing
nv = {}
for el in result1.split('\r\n'):
data = el.split('=')
if len(data)==2:
key = data[0]
val = data[1]
nv[key.strip()]=val.strip()
In [65]:
#check the resulting listing
print(nv)
print()
print(nv['unexpanded_descriptors'])
{'start_signature': "b'BUFR'", 'length': '249954', 'edition': '4', 'section_length': '249841', 'master_table_number': '0', 'originating_centre': '254', 'originating_subcentre': '0', 'update_sequence_number': '0', 'is_section2_presents': 'False', 'flag_bits': "'000000'", 'data_category': '5', 'data_i18n_subcategory': '255', 'data_local_subcategory': '70', 'master_table_version': '12', 'local_table_version': '0', 'year': '2024', 'month': '6', 'day': '12', 'hour': '11', 'minute': '15', 'second': '0', 'local_bytes': "b''", 'reserved_bits': "'00000000'", 'n_subsets': '13973', 'is_observation': 'True', 'is_compressed': 'True', 'unexpanded_descriptors': '[301071, 301011, 301013, 5031, 6031, 301021, 7024, 13044, 13045, 201129, 13042, 201000, 13046, 13016, 33007, 7004, 7004, 201134, 202130, 13016, 202000, 201000, 7004, 7004, 201134, 202130, 13016, 202000, 201000, 7004, 7004, 201134, 202130, 13016, 202000, 201000]'}
[301071, 301011, 301013, 5031, 6031, 301021, 7024, 13044, 13045, 201129, 13042, 201000, 13046, 13016, 33007, 7004, 7004, 201134, 202130, 13016, 202000, 201000, 7004, 7004, 201134, 202130, 13016, 202000, 201000, 7004, 7004, 201134, 202130, 13016, 202000, 201000]
In [66]:
#remove the [] and , and then loop through the descriptors listing
for BUFR_parameter in nv['unexpanded_descriptors'].lstrip('[').rstrip(']').split(','):
BUFR_parameter = BUFR_parameter.strip()
result1 = subprocess.run(['pybufrkit', 'lookup', '-l', BUFR_parameter], capture_output=True)
result1 = result1.stdout.decode('utf-8')
print(result1)
301071 (Satellite identifier/Generating resolution)
001007 SATELLITE IDENTIFIER
001031 IDENTIFICATION OF ORIGINATING/GENERATING CENTRE
002020 SATELLITE CLASSIFICATION
002028 SEGMENT SIZE AT NADIR IN X-DIRECTION
002029 SEGMENT SIZE AT NADIR IN Y-DIRECTION
301011 (Year, month, day)
004001 YEAR
004002 MONTH
004003 DAY
301013 (Hour, minute, second)
004004 HOUR
004005 MINUTE
004006 SECOND
005031 ROW NUMBER, Numeric, 0, 0, 12
006031 COLUMN NUMBER, Numeric, 0, 0, 12
301021 (Latitude/longitude (high accuracy))
005001 LATITUDE (HIGH ACCURACY)
006001 LONGITUDE (HIGH ACCURACY)
007024 SATELLITE ZENITH ANGLE, deg, 2, -9000, 15
013044 K INDEX, K, 0, -30, 8
013045 KO INDEX, K, 0, -30, 8
201129
013042 PARCEL LIFTED INDEX (TO 500 HPA), K, 0, -20, 6
201000
013046 MAXIMUM BUOYANCY, K, 0, -30, 8
013016 PRECIPITABLE WATER, kg m-2, 0, 0, 7
033007 PER CENT CONFIDENCE, %, 0, 0, 7
007004 PRESSURE, Pa, -1, 0, 14
007004 PRESSURE, Pa, -1, 0, 14
201134
202130
013016 PRECIPITABLE WATER, kg m-2, 0, 0, 7
202000
201000
007004 PRESSURE, Pa, -1, 0, 14
007004 PRESSURE, Pa, -1, 0, 14
201134
202130
013016 PRECIPITABLE WATER, kg m-2, 0, 0, 7
202000
201000
007004 PRESSURE, Pa, -1, 0, 14
007004 PRESSURE, Pa, -1, 0, 14
201134
202130
013016 PRECIPITABLE WATER, kg m-2, 0, 0, 7
202000
201000
In [67]:
# see: unexpanded_descriptors = '013044'
BUFR_parameter = '013044'
In [68]:
result_BUFR = subprocess.run(['pybufrkit', 'lookup', '-l', BUFR_parameter], capture_output=True)
result_BUFR = result_BUFR.stdout.decode('utf-8')
print(result_BUFR)
013044 K INDEX, K, 0, -30, 8
In [69]:
param = DataQuerent(NodePathParser()).query(bufr_message, BUFR_parameter)
print(param.results)
OrderedDict({0: [-16], 1: [-15], 2: [-15], 3: [-15], 4: [-15], 5: [-15], 6: [-15], 7: [-16], 8: [-16], 9: [-16], 10: [-14], 11: [-14], 12: [-13], 13: [-12], 14: [-12], 15: [-12], 16: [-9], 17: [-8], 18: [-8], 19: [-8], 20: [-7], 21: [-7], 22: [-7], 23: [-7], 24: [-7], 25: [-7], 26: [-7], 27: [-7], 28: [-7], 29: [-7], 30: [-7], 31: [-6], 32: [-6], 33: [-6], 34: [-6], 35: [-6], 36: [-6], 37: [-6], 38: [-6], 39: [-6], 40: [-6], 41: [-5], 42: [-5], 43: [-5], 44: [-5], 45: [-5], 46: [-4], 47: [-4], 48: [-4], 49: [-4], 50: [-3], 51: [-3], 52: [-3], 53: [-2], 54: [-2], 55: [-2], 56: [-1], 57: [-1], 58: [-1], 59: [1], 60: [1], 61: [1], 62: [1], 63: [1], 64: [1], 65: [1], 66: [1], 67: [1], 68: [1], 69: [0], 70: [0], 71: [0], 72: [-1], 73: [-2], 74: [-2], 75: [-3], 76: [-3], 77: [-4], 78: [-4], 79: [-5], 80: [-5], 81: [-5], 82: [-6], 83: [-6], 84: [-7], 85: [-7], 86: [-8], 87: [-8], 88: [-8], 89: [-8], 90: [-8], 91: [-9], 92: [-9], 93: [-9], 94: [-9], 95: [-9], 96: [-9], 97: [-9], 98: [-9], 99: [-9], 100: [-9], 101: [-9], 102: [-10], 103: [-10], 104: [-10], 105: [-10], 106: [-10], 107: [-10], 108: [-10], 109: [-10], 110: [-10], 111: [-10], 112: [-11], 113: [-11], 114: [-11], 115: [-10], 116: [-10], 117: [-10], 118: [-9], 119: [-9], 120: [-9], 121: [-8], 122: [-8], 123: [-8], 124: [-8], 125: [-8], 126: [-8], 127: [-8], 128: [-8], 129: [-8], 130: [-8], 131: [-8], 132: [-9], 133: [-9], 134: [-9], 135: [-10], 136: [-10], 137: [-11], 138: [-11], 139: [-10], 140: [-10], 141: [-9], 142: [-9], 143: [-9], 144: [-10], 145: [-10], 146: [-10], 147: [-10], 148: [-10], 149: [-9], 150: [-8], 151: [-7], 152: [-7], 153: [-8], 154: [-8], 155: [-8], 156: [-8], 157: [-8], 158: [-9], 159: [-10], 160: [-10], 161: [-11], 162: [-13], 163: [-14], 164: [-14], 165: [-14], 166: [-14], 167: [-14], 168: [-14], 169: [-14], 170: [-14], 171: [-14], 172: [-14], 173: [-14], 174: [-13], 175: [-13], 176: [-13], 177: [-13], 178: [-12], 179: [-12], 180: [-12], 181: [-12], 182: [-13], 183: [-13], 184: [-14], 185: [-13], 186: [-13], 187: [-15], 188: [-14], 189: [-16], 190: [-16], 191: [-17], 192: [-15], 193: [-16], 194: [-17], 195: [-17], 196: [-16], 197: [-16], 198: [-16], 199: [-15], 200: [-15], 201: [-15], 202: [-15], 203: [-15], 204: [-14], 205: [-14], 206: [-14], 207: [-14], 208: [-15], 209: [-15], 210: [-15], 211: [-15], 212: [-16], 213: [-15], 214: [-15], 215: [-14], 216: [-14], 217: [-14], 218: [-13], 219: [-13], 220: [-13], 221: [-13], 222: [-13], 223: [-13], 224: [-13], 225: [-13], 226: [-13], 227: [-13], 228: [-13], 229: [-13], 230: [-13], 231: [-13], 232: [-13], 233: [-13], 234: [-13], 235: [-13], 236: [-13], 237: [-13], 238: [-13], 239: [-14], 240: [-14], 241: [-15], 242: [-16], 243: [-17], 244: [-19], 245: [-20], 246: [-20], 247: [-19], 248: [-19], 249: [-18], 250: [-18], 251: [-17], 252: [-17], 253: [-17], 254: [-17], 255: [-17], 256: [-13], 257: [-17], 258: [-15], 259: [-18], 260: [-14], 261: [-17], 262: [-17], 263: [-16], 264: [-14], 265: [-11], 266: [-7], 267: [-5], 268: [-2], 269: [1], 270: [3], 271: [5], 272: [7], 273: [7], 274: [8], 275: [9], 276: [7], 277: [5], 278: [2], 279: [-1], 280: [-3], 281: [-5], 282: [-8], 283: [1], 284: [7], 285: [11], 286: [8], 287: [None], 288: [None], 289: [None], 290: [None], 291: [-26], 292: [-26], 293: [-29], 294: [-29], 295: [-30], 296: [-30], 297: [-30], 298: [-28], 299: [-28], 300: [-28], 301: [-28], 302: [-28], 303: [-28], 304: [-26], 305: [-26], 306: [-26], 307: [-25], 308: [-16], 309: [-16], 310: [-13], 311: [-13], 312: [-12], 313: [-12], 314: [-11], 315: [-11], 316: [-11], 317: [-11], 318: [-8], 319: [-8], 320: [-8], 321: [-7], 322: [-7], 323: [-7], 324: [-7], 325: [-7], 326: [-7], 327: [-7], 328: [-7], 329: [-7], 330: [-7], 331: [-7], 332: [-7], 333: [-7], 334: [-7], 335: [-7], 336: [-7], 337: [-7], 338: [-7], 339: [-7], 340: [-7], 341: [-7], 342: [-7], 343: [-7], 344: [-6], 345: [-6], 346: [-6], 347: [-6], 348: [-6], 349: [-6], 350: [-5], 351: [-5], 352: [-5], 353: [-5], 354: [-5], 355: [-4], 356: [-4], 357: [-4], 358: [-3], 359: [-3], 360: [-3], 361: [-3], 362: [-2], 363: [-2], 364: [-1], 365: [-1], 366: [-1], 367: [-1], 368: [0], 369: [1], 370: [1], 371: [1], 372: [1], 373: [1], 374: [1], 375: [1], 376: [1], 377: [1], 378: [0], 379: [0], 380: [-1], 381: [-1], 382: [-2], 383: [-3], 384: [-3], 385: [-4], 386: [-4], 387: [-5], 388: [-5], 389: [-5], 390: [-6], 391: [-6], 392: [-7], 393: [-7], 394: [-7], 395: [-7], 396: [-7], 397: [-7], 398: [-7], 399: [-8], 400: [-8], 401: [-8], 402: [-8], 403: [-8], 404: [-8], 405: [-8], 406: [-8], 407: [-8], 408: [-8], 409: [-9], 410: [-9], 411: [-9], 412: [-9], 413: [-9], 414: [-9], 415: [-9], 416: [-9], 417: [-9], 418: [-10], 419: [-10], 420: [-10], 421: [-10], 422: [-9], 423: [-9], 424: [-9], 425: [-8], 426: [-8], 427: [-8], 428: [-7], 429: [-7], 430: [-7], 431: [-8], 432: [-8], 433: [-8], 434: [-9], 435: [-10], 436: [-10], 437: [-10], 438: [-10], 439: [-9], 440: [-9], 441: [-9], 442: [-9], 443: [-9], 444: [-10], 445: [-10], 446: [-10], 447: [-11], 448: [-10], 449: [-9], 450: [-9], 451: [-7], 452: [-7], 453: [-7], 454: [-8], 455: [-8], 456: [-8], 457: [-8], 458: [-9], 459: [-9], 460: [-11], 461: [-14], 462: [-14], 463: [-14], 464: [-12], 465: [-14], 466: [-14], 467: [-14], 468: [-14], 469: [-14], 470: [-14], 471: [-14], 472: [-13], 473: [-13], 474: [-12], 475: [-12], 476: [-12], 477: [-12], 478: [-12], 479: [-12], 480: [-11], 481: [-13], 482: [-12], 483: [-13], 484: [-14], 485: [-16], 486: [-17], 487: [-15], 488: [-15], 489: [-17], 490: [-17], 491: [-16], 492: [-16], 493: [-16], 494: [-15], 495: [-15], 496: [-15], 497: [-15], 498: [-14], 499: [-14], 500: [-14], 501: [-14], 502: [-14], 503: [-14], 504: [-15], 505: [-15], 506: [-15], 507: [-15], 508: [-15], 509: [-15], 510: [-14], 511: [-14], 512: [-14], 513: [-13], 514: [-13], 515: [-13], 516: [-13], 517: [-13], 518: [-13], 519: [-13], 520: [-13], 521: [-13], 522: [-13], 523: [-13], 524: [-13], 525: [-13], 526: [-12], 527: [-12], 528: [-12], 529: [-12], 530: [-12], 531: [-12], 532: [-12], 533: [-12], 534: [-13], 535: [-13], 536: [-13], 537: [-14], 538: [-16], 539: [-15], 540: [-18], 541: [-18], 542: [-18], 543: [-18], 544: [-18], 545: [-17], 546: [-17], 547: [-17], 548: [-17], 549: [-16], 550: [-16], 551: [-17], 552: [-17], 553: [-17], 554: [-18], 555: [-18], 556: [-14], 557: [-17], 558: [-17], 559: [-16], 560: [-9], 561: [-7], 562: [-5], 563: [-3], 564: [0], 565: [2], 566: [5], 567: [7], 568: [8], 569: [8], 570: [9], 571: [9], 572: [6], 573: [3], 574: [0], 575: [-2], 576: [-4], 577: [-7], 578: [-1], 579: [5], 580: [10], 581: [11], 582: [None], 583: [None], 584: [None], 585: [None], 586: [None], 587: [-26], 588: [-26], 589: [-27], 590: [-29], 591: [-30], 592: [-30], 593: [-30], 594: [-30], 595: [-30], 596: [-29], 597: [-29], 598: [-29], 599: [-29], 600: [-29], 601: [-29], 602: [-29], 603: [-29], 604: [-28], 605: [-28], 606: [-28], 607: [-13], 608: [-10], 609: [-9], 610: [-8], 611: [-8], 612: [-7], 613: [-7], 614: [-7], 615: [-7], 616: [-7], 617: [-7], 618: [-7], 619: [-7], 620: [-6], 621: [-6], 622: [-6], 623: [-7], 624: [-7], 625: [-7], 626: [-7], 627: [-7], 628: [-7], 629: [-7], 630: [-7], 631: [-7], 632: [-7], 633: [-7], 634: [-6], 635: [-6], 636: [-6], 637: [-6], 638: [-6], 639: [-6], 640: [-6], 641: [-5], 642: [-5], 643: [-5], 644: [-5], 645: [-4], 646: [-4], 647: [-4], 648: [-3], 649: [-3], 650: [-3], 651: [-3], 652: [-1], 653: [-1], 654: [1], 655: [1], 656: [1], 657: [1], 658: [1], 659: [1], 660: [1], 661: [0], 662: [0], 663: [0], 664: [-3], 665: [-4], 666: [-5], 667: [-5], 668: [-5], 669: [-6], 670: [-6], 671: [-6], 672: [-6], 673: [-6], 674: [-6], 675: [-6], 676: [-6], 677: [-6], 678: [-7], 679: [-7], 680: [-7], 681: [-7], 682: [-7], 683: [-7], 684: [-7], 685: [-7], 686: [-8], 687: [-8], 688: [-8], 689: [-8], 690: [-8], 691: [-8], 692: [-8], 693: [-8], 694: [-9], 695: [-9], 696: [-9], 697: [-9], 698: [-9], 699: [-8], 700: [-8], 701: [-8], 702: [-7], 703: [-7], 704: [-7], 705: [-7], 706: [-7], 707: [-7], 708: [-7], 709: [-8], 710: [-8], 711: [-9], 712: [-10], 713: [-10], 714: [-10], 715: [-9], 716: [-9], 717: [-9], 718: [-9], 719: [-9], 720: [-9], 721: [-9], 722: [-9], 723: [-9], 724: [-10], 725: [-10], 726: [-10], 727: [-9], 728: [-9], 729: [-7], 730: [-7], 731: [-8], 732: [-8], 733: [-8], 734: [-8], 735: [-9], 736: [-11], 737: [-15], 738: [-14], 739: [-14], 740: [-14], 741: [-14], 742: [-14], 743: [-14], 744: [-14], 745: [-14], 746: [-14], 747: [-14], 748: [-13], 749: [-13], 750: [-12], 751: [-12], 752: [-12], 753: [-11], 754: [-11], 755: [-12], 756: [-13], 757: [-12], 758: [-13], 759: [-13], 760: [-14], 761: [-16], 762: [-16], 763: [-16], 764: [-17], 765: [-17], 766: [-17], 767: [-17], 768: [-16], 769: [-16], 770: [-15], 771: [-15], 772: [-15], 773: [-14], 774: [-14], 775: [-14], 776: [-14], 777: [-14], 778: [-14], 779: [-14], 780: [-14], 781: [-14], 782: [-15], 783: [-15], 784: [-15], 785: [-15], 786: [-15], 787: [-14], 788: [-14], 789: [-13], 790: [-13], 791: [-13], 792: [-13], 793: [-13], 794: [-13], 795: [-13], 796: [-13], 797: [-13], 798: [-13], 799: [-12], 800: [-12], 801: [-12], 802: [-12], 803: [-12], 804: [-12], 805: [-11], 806: [-11], 807: [-11], 808: [-11], 809: [-11], 810: [-11], 811: [-11], 812: [-12], 813: [-12], 814: [-13], 815: [-14], 816: [-14], 817: [-15], 818: [-17], 819: [-17], 820: [-17], 821: [-17], 822: [-17], 823: [-16], 824: [-16], 825: [-16], 826: [-16], 827: [-16], 828: [-16], 829: [-16], 830: [-17], 831: [-17], 832: [-18], 833: [-14], 834: [-17], 835: [-17], 836: [-16], 837: [-13], 838: [-8], 839: [-6], 840: [-4], 841: [-1], 842: [2], 843: [4], 844: [7], 845: [8], 846: [9], 847: [9], 848: [10], 849: [8], 850: [5], 851: [2], 852: [-1], 853: [-3], 854: [-6], 855: [-5], 856: [4], 857: [9], 858: [13], 859: [None], 860: [None], 861: [None], 862: [None], 863: [None], 864: [-26], 865: [-26], 866: [-26], 867: [-27], 868: [-28], 869: [-29], 870: [-30], 871: [-29], 872: [-29], 873: [-29], 874: [-29], 875: [-29], 876: [-28], 877: [-28], 878: [-27], 879: [-11], 880: [-10], 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14169: [-2], 14170: [-1]})
In [70]:
vals=[param.results[key][0] for key in param.results.keys()]
#print(vals)
In [71]:
#Relevant coordinate variables are: latitude (code 005001), longitude (code 006001)
#Relevant parameters is: K_Index (code 013044)
lats = np.array([])
lons = np.array([])
K_Indexs = np.array([])
for message in messages:
LAT_result = DataQuerent(NodePathParser()).query(message, '005001')
lats=np.concatenate((lats, np.array([LAT_result.results[key][0] for key in LAT_result.results.keys()])))
LON_result = DataQuerent(NodePathParser()).query(message, '006001')
lons=np.concatenate((lons,np.array([LON_result.results[key][0] for key in LON_result.results.keys()])))
K_Index_result = DataQuerent(NodePathParser()).query(message, '013044')
K_Indexs=np.concatenate((K_Indexs,np.array([K_Index_result.results[key][0] for key in K_Index_result.results.keys()])))
In [72]:
# convert numpy array to pandas dataframe
Kindex_dataset = pd.DataFrame({'LAT': lats, 'LON': lons, 'K_index': K_Indexs})
Kindex_dataset
Out[72]:
| LAT | LON | K_index | |
|---|---|---|---|
| 0 | 23.40893 | -65.32246 | 26 |
| 1 | 23.41195 | -63.72351 | 27 |
| 2 | 23.43233 | -64.05890 | 26 |
| 3 | 23.45291 | -64.40001 | 29 |
| 4 | 23.47439 | -64.75106 | 26 |
| ... | ... | ... | ... |
| 461471 | -14.33687 | -27.27770 | -3 |
| 461472 | -14.33912 | -27.37881 | -3 |
| 461473 | -14.34151 | -27.48032 | -3 |
| 461474 | -14.34616 | -27.68346 | -2 |
| 461475 | -14.34853 | -27.78534 | -1 |
461476 rows × 3 columns
In [73]:
#Scatter plot of parameter selected
plt.figure(figsize=(6,6))
colvals = preprocessing.minmax_scale(K_Indexs)
plt.scatter(lons, lats, color=cm.rainbow(colvals), marker='.')
plt.xlabel('x')
plt.ylabel('y')
plt.xlim(-70, 70)
plt.ylim(-70, 70)
plt.show()
In [74]:
#create shape file
#ensure all values are of dtype float (e.g. in case there is a missing value!)
Kindex_dataset['K_index']=Kindex_dataset['K_index'].astype(float)
In [75]:
# Create geometry column
geometry = [Point(xy) for xy in zip(Kindex_dataset['LON'], Kindex_dataset['LAT'])]
In [76]:
# Create GeoDataFrame
gdf = gpd.GeoDataFrame(Kindex_dataset, geometry=geometry)
In [77]:
# Set the coordinate reference system (CRS), WGS84
gdf.set_crs(epsg=4326, inplace=True)
Out[77]:
| LAT | LON | K_index | geometry | |
|---|---|---|---|---|
| 0 | 23.40893 | -65.32246 | 26.0 | POINT (-65.32246 23.40893) |
| 1 | 23.41195 | -63.72351 | 27.0 | POINT (-63.72351 23.41195) |
| 2 | 23.43233 | -64.05890 | 26.0 | POINT (-64.0589 23.43233) |
| 3 | 23.45291 | -64.40001 | 29.0 | POINT (-64.40001 23.45291) |
| 4 | 23.47439 | -64.75106 | 26.0 | POINT (-64.75106 23.47439) |
| ... | ... | ... | ... | ... |
| 461471 | -14.33687 | -27.27770 | -3.0 | POINT (-27.2777 -14.33687) |
| 461472 | -14.33912 | -27.37881 | -3.0 | POINT (-27.37881 -14.33912) |
| 461473 | -14.34151 | -27.48032 | -3.0 | POINT (-27.48032 -14.34151) |
| 461474 | -14.34616 | -27.68346 | -2.0 | POINT (-27.68346 -14.34616) |
| 461475 | -14.34853 | -27.78534 | -1.0 | POINT (-27.78534 -14.34853) |
461476 rows × 4 columns
In [78]:
# Write to shapefile
gdf.to_file(work_dir+'/K_index.shp')
In [79]:
#check your folder and note the additional messages created
#remove obsolete files, here the seperate messages extracted
for p in glob(work_dir+'/*.bin.*', recursive=True):
if os.path.isfile(p):
os.remove(p)
Review the new shape file created using ilwis386 or qgis¶
GRIB data processing¶
Grib data from the CRM Product¶
For information on this product see: https://navigator.eumetsat.int/product/EO:EUM:DAT:MSG:CRM?query=crm%20msg&s=advanced
In [80]:
#dataset
file_in = work_dir+r'/W_XX-EUMETSAT-Darmstadt,SING+LEV+SAT,MET10+CRM_C_EUMG_20240123120000_3.bin'
In [81]:
CRM = ilwis.RasterCoverage(file_in)
print(CRM.size())
#retrieve the 1.6 micron layer of the dataset
b16 = ilwis.do('selection',CRM,"rasterbands(5)")
print(b16.size())
Size(3712, 3712, 7) Size(3712, 3712, 1)
In [82]:
#check coordinate system information as well as the map extent - bounding coordinates
coordSys = b16.coordinateSystem()
print(coordSys.toWKT())
print()
print(b16.envelope())
PROJCS["_ANONYMOUS_1004820",GEOCS["_ANONYMOUS_1004820",DATUM["Unknown Datum,ELLIPSOID["User Defined",6378140.000000000000,298.252981061492],PRIMEM["Greenwich",0, AUTHORITY["EPSG",8901"]]],PROJECTION["GEOS"],PARAMETER[false_easting,0],PARAMETER[false_northing,0],PARAMETER[central_meridian,0],UNIT[meter,1.0]] -5570248.477583 -5567248.074417 5567248.074417 5570248.477583
In [83]:
#retrieve some statistics and the data type
stats = b16.statistics(ilwis.PropertySets.pHISTOGRAM)
min_val = stats[ilwis.PropertySets.pMIN]
max_val = stats[ilwis.PropertySets.pMAX]
print(min_val)
print(max_val)
print()
datadef = b16.datadef()
print(datadef.domain())
0.0 148.0 value
In [84]:
#convert ilwis array to numpy array
#note the poor image visualization due to the fact that values are not properly scaled - stretched
b16_2np = np.fromiter(iter(b16), np.float64, b16.size().linearSize())
b16_2np = b16_2np.reshape((b16.size().ysize, b16.size().xsize))
# Plot the 2D array using matplotlib
plt.imshow(b16_2np, vmin=min_val, vmax=max_val, cmap='grey')
plt.title('Selected Layer of Raster Stack')
plt.colorbar(label='Pixel values', shrink=0.6)
plt.axis('off')
plt.show()
In [85]:
#dislay the color composite, aslo using the green and blue components
#for display using matplotlib
rows = b16.size().ysize
cols = b16.size().xsize
linear_size = cols*rows
print(rows)
print(cols)
print(linear_size)
red = ilwis.do('selection',CRM,"rasterbands(5)")
green = ilwis.do('selection',CRM,"rasterbands(4)")
blue = ilwis.do('selection',CRM,"rasterbands(3)")
redfc = np.fromiter(iter(red), np.ubyte, linear_size)
redfc = redfc.reshape(rows,cols)
greenfc = np.fromiter(iter(green), np.ubyte, linear_size)
greenfc = greenfc.reshape(rows,cols)
bluefc = np.fromiter(iter(blue), np.ubyte, linear_size)
bluefc = bluefc.reshape(rows,cols)
cc = np.dstack((redfc, greenfc, bluefc))
3712 3712 13778944
In [86]:
# Plot the 2D array using matplotlib
plt.imshow(cc)
plt.title('Composite of Raster Stack')
plt.axis('off')
plt.show()
Retrieve the original data once more, use a mask and assign the background to ilwis no data - nan, conduct a linear contract stretch and save the resulting maplist. Check the results also using ilwis386
In [87]:
#retrieve the 0.6 and 0.8 micron and 1.6 layers of the dataset
crm_cc = ilwis.do('selection',CRM,"rasterbands(3,4,5)")
print(crm_cc.size())
Size(3712, 3712, 3)
In [88]:
#import a mask to remove the background
mask = ilwis.RasterCoverage('MSG_0_mask.mpr') # background value of mask = 0
In [89]:
crm_mod = ilwis.do('mapcalc', 'iff(@1>0,@2,?)', mask, crm_cc)
In [90]:
#stretch the rasterbands contained in crm_mod
multiple_stretch = []
multiple_bands = ilwis.do('selection',crm_mod,"rasterbands(0..2)") # for specific bands use band number as "rasterbands(0,1,2,6)"
ls = ilwis.do('linearstretch',multiple_bands, 1)
mb_stretch = ilwis.do('setvaluerange', ls, 0, 255, 1)
In [116]:
#for all pixels with no data set the value to 255 (to create a white background in matplotlib)
rcNew2 = ilwis.do('mapcalc', 'iff(@1==?,255, @1)',mb_stretch)
In [126]:
red = ilwis.do('selection',rcNew2,"rasterbands(2)")
green = ilwis.do('selection',rcNew2,"rasterbands(1)")
blue = ilwis.do('selection',rcNew2,"rasterbands(0)")
redfc = np.fromiter(iter(red), np.ubyte, linear_size)
redfc = redfc.reshape(rows,cols)
redfc_mask = np.ma.masked_where(redfc == 255, redfc)
greenfc = np.fromiter(iter(green), np.ubyte, linear_size)
greenfc = greenfc.reshape(rows,cols)
greenfc_mask = np.ma.masked_where(greenfc == 255, greenfc)
bluefc = np.fromiter(iter(blue), np.ubyte, linear_size)
bluefc = bluefc.reshape(rows,cols)
bluefc_mask = np.ma.masked_where(bluefc == 255, bluefc)
cc1 = np.dstack((redfc_mask, greenfc_mask, bluefc_mask))
In [129]:
# Plot the stretched 3D array using matplotlib
plt.imshow(cc1,extent=[xmin/1000, xmax/1000, ymin/1000, ymax/1000], origin='upper')
plt.xlabel('X Coordinate (km)')
plt.ylabel('Y Coordinate (km)')
plt.title('CRM Map in RGB (1.6, 0.8 and 0.6 Micron) ')
plt.show()
In [130]:
#store the results - check also using ilwis386
mb_stretch.store('crm_cc.mpl')
Convert a map with values to classes - example Cloud Map¶
See further product information at https://navigator.eumetsat.int/product/EO:EUM:DAT:MSG:CLM?query=clm%20msg&s=advanced
In [92]:
# CLM input data
file_in = work_dir+'/W_XX-EUMETSAT-Darmstadt,SING+LEV+SAT,MET10+CLM_C_EUMG_20240612111500_3.bin'
In [93]:
CLM = ilwis.RasterCoverage(file_in)
print(CLM.size())
#retrieve the 1.6 micron layer of the dataset
clm_data = ilwis.do('selection',CLM,"rasterbands(0)")
print(clm_data.size())
Size(3712, 3712, 1) Size(3712, 3712, 1)
In [94]:
#convert ilwis array to numpy array
clm_data_2np = np.fromiter(iter(clm_data), np.float64, clm_data.size().linearSize())
clm_data_2np = clm_data_2np.reshape((clm_data.size().ysize, clm_data.size().xsize))
# Plot the 2D array using matplotlib
plt.imshow(clm_data_2np, vmin=0, vmax=3, cmap='jet')
plt.title('Selected Layer of Raster Stack')
plt.colorbar(label='Pixel values', shrink=0.6)
plt.axis('off')
plt.show()
In [95]:
#set background to no data
rcNew = ilwis.do('mapcalc', 'iff(@1==3,?,@1)', clm_data)
In [96]:
# create a new NumericItemRange and fill it with required class items
cltrange = ilwis.NumericItemRange()
cltrange.add(('Water', 0.0, 0.99)) #0
cltrange.add(('Land', 1.0, 1.99)) #1
cltrange.add(('Cloud', 2.0, 2.99)) #2
In [97]:
#assign the range to a new ItemDomain, slice the map and store the results
#check the reclassified map created using ilwis386 and note the use of the ilwis pre-set "Representation" MPEF_CLMClass.rp# renamed to cloud_class.rp#
#create a new domain from the ranges specified above
clt_dom = ilwis.ItemDomain(cltrange)
#perform the raster slicing - assign the values to the domain classes based on the initial value
cloud_cl = ilwis.do('sliceraster', rcNew, clt_dom)
#create a domain with the idential name
cloud_cl.datadef().domain().name('cloud_class')
#store the map
cloud_cl.store('cloud_class.mpr')
#use ilwis colour representation template available within the sample data set and assign it the name of the output map created
src_file = work_dir+'/MPEF_CLMClass.rp#'
dst_file = work_dir+'/cloud_class.rp#'
shutil.copy(src_file, dst_file)
Out[97]:
'd:\\jupyter\\notebook_scripts\\ilwispy_tutorial\\tutorial_release/BUFR_GRIB_data/cloud_class.rp#'